Gas-mapping 3d imager measurement techniques and method of data processing

ABSTRACT

Measurement approaches and data analysis methods are disclosed for combining 3D topographic data with spatially-registered gas concentration data to increase the efficiency of gas monitoring and leak detection tasks. Here, the metric for efficiency is defined as reducing the measurement time required to achieve the detection, or non-detection, of a gas leak with a desired confidence level. Methods are presented for localizing and quantifying detected gas leaks. Particular attention is paid to the combination of 3D spatial data with path-integrated gas concentration measurements acquired using remote gas sensing technologies, as this data can be used to determine the path-averaged gas concentration between the sensor and points in the measurement scene. Path-averaged gas concentration data is useful for finding and quantifying localized regions of elevated (or anomalous) gas concentration making it ideal for a variety of applications including: oil and gas pipeline monitoring, facility leak and emissions monitoring, and environmental monitoring.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of pending U.S. patent applicationSer. No. 16/734,769, filed Jan. 6, 2020, which is a continuation ofissued U.S. patent application Ser. No. 15/285,787 filed Oct. 5, 2016and issued as U.S. Pat. No. 10,527,412, on Jan. 7, 2020, which claimsthe benefit of provisional U.S. application Ser. No. 62/237,992, filedOct. 6, 2015. The aforementioned applications are incorporated herein byreference, in their entirety, for any purposes.

STATEMENT REGARDING RESEARCH & DEVELOPMENT

This invention was made with government support under DE-AR0000544awarded by the Department of Energy. The government has certain rightsin the invention.

FIELD OF THE INVENTION

The present invention generally relates to the application of 3D spatialdata and gas concentration data to perform gas leak detection andmonitoring.

BACKGROUND OF THE INVENTION

Improved gas detection and monitoring technologies are needed for avariety of emerging applications including:

(1) leak detection and quantification for oil and gas and chemicalprocessing infrastructure,

(2) emissions monitoring from landfill and waste treatment facilities,

(3) monitoring and verification for carbon sequestration, and

(4) environmental terrestrial monitoring to better understand the carboncycle.

Sensor solutions to meet the needs of emerging applications must providecost effective, large-area, high-sensitivity, and quantitative detectionof target gases, and will likely require mobile sensor platforms thatincorporate spatial data such as GPS and GIS for spatial-registering,mapping and time-stamping of acquired datasets. For many applications,advanced measurement capabilities such as leak localization and fluxestimation are also desired. The invention disclosed herein describesmeasurement techniques and data analysis methods that can be implementedusing combinations of existing 3D topography and gas concentrationsensor technologies to meet emerging measurement needs.

Over the past three decades 3D topographical scanning through such meansas LiDAR and photogrammetry has become a powerful tool for large-areasurveying, mapping and infrastructure monitoring. Recently, the cost ofLiDAR and photogrammetric sensors for producing high-quality 3D spatialdata have reached a point where the application and prevalence of 3Ddata has become widespread. Commercially available sensors can now mapterrain and infrastructure with several centimeter precision fromdistances exceeding 1000 feet and at measurement rates exceeding 500,000points per second. Data acquired with these sensors is used to createseveral distinct data representations of a measured topographic sceneincluding: point clouds (See, e.g., the Point Cloud Library), digitalsurface models, and digital elevation models (See, e.g., OpenDEM). Theemergence of 3D data types has been accompanied by the development ofvast body of image processing software, such as the Point Cloud Library,for rapid and sophisticated exploitation of 3D data. Examples of commonprocessing tasks for 3D point data include organization of the data inan efficiently searchable tree structure, segmentation of like objectswithin a scene, detection of occluded portions of a scene from aspecified viewing location, surface reconstruction, shape detection andidentification of objects in a scene (See, e.g., the Point CloudLibrary). The combination of high-quality 3D data with these processingand analysis tools has the potential to play an important role indefining new and valuable measurement procedures for gas detection,localization, and quantification tasks.

SUMMARY OF THE INVENTION

A method is provided for reducing the time needed to monitor for gasleaks, comprising: utilizing 3D spatial data to identify regions and/orstructures of a scene for gas monitoring and/or regions that may beoccluded from view; utilizing the identified regions and/or structuresof the scene to determine a gas sensing measurement procedure thatexhibits reduced measurement time and/or improved detection confidencecompared to a gas sensing measurement procedure created withoutknowledge of the 3D spatial data of the scene; and utilizing thedetermined gas sensing measurement procedure to perform gas sensing of ascene.

The gas sensing measurement may be performed using a remote gas sensor.

The determined gas sensing measurement procedure may include occlusionprocessing of the 3D spatial data.

The identification of the regions and/or structures of the scene mayinclude segmentation of structures or features in the 3D spatial data.

The identification of the regions and/or structures of the scene mayinclude shape detection or feature identification of structures orfeatures in the 3D spatial data.

The determined gas sensing measurement procedure is performed with amobile gas sensor;

A method is provided of identifying the leak location or leakingcomponent comprising: acquiring new 3D data of a scene or accessingpreviously acquired 3D data of a scene; acquiring spatially registeredgas concentration measurements within a scene or accessing previouslyacquired spatially registered gas concentration measurements within ascene; and determining a location of a gas leak source by utilizing 3Dspatial data of a scene and spatially registered gas concentrationmeasurements within the scene.

The determined gas leak source location may be combined with componentlocation information and/or feature identification algorithms applied tothe 3D spatial data to determine a component corresponding to the leaksource.

The determined gas leak source location may involve the use of winddata.

The determined gas leak source location may involve occlusion processingof the scene.

Gas sensing measurements from a plurality of viewing locations may beused to improve the determination of a gas leak source location or thelocation and extent of a gas plume.

A method is provided for quantifying a detected leak comprising:acquiring new 3D data of a scene or accessing previously acquired 3Ddata of a scene; acquiring spatially registered gas concentrationmeasurements within a scene or accessing previously acquired spatiallyregistered gas concentration measurements; and determining an anomalousgas quantity in the scene that is greater or less than the backgroundgas quantity in the scene.

The determined anomalous gas quantity may be calculated by firstsubtracting the background path-averaged gas concentration that iseither measured or otherwise known to be in the scene from the measuredpath-averaged gas concentration data to derive path-integrated anomalousgas concentration data, the path-integrated anomalous gas concentrationdata is then integrated over the spatial coordinates of the measurementscene to determine the anomalous gas quantity.

Gas sensing measurements from a plurality of viewing locations may beused to improve the accuracy of the anomalous gas quantitydetermination.

A method is provided of quantifying a gas flux comprising: scanning alaser beam across a gas plume; using the scattered light from thescanned laser beam to determine gas concentration at a plurality oflocations through the gas plume; determining or assuming wind data nearthe gas plume; determining a gas flux by utilizing the determined gasconcentration and the determined or assumed wind data.

The scanned laser beam may form a boundary that encloses the leaksource.

The method may further comprise: performing measurements of a plume frommore than one position to determine a location of the plume and toimprove the leak rate estimate.

A method is provided of quantifying a detected gas flux comprising:acquiring new range data for a scene or accessing previously acquiredrange data for a scene; acquiring spatially registered spatiallyregistered path-integrated gas absorption measurements within a scene oraccessing previously acquired spatially registered spatially registeredpath-integrated gas absorption measurements; and determining a gas fluxby utilizing range information with a closed-volume scan pattern ofspatially registered path-integrated gas absorption measurements.

The closed-volume scan pattern measurement may be performed from morethan one position to determine the plume location and improve the leakrate estimate.

A method is provided of determining the position of a gas plume in 3Dspace comprising: acquiring new range data for a scene or accessingpreviously acquired range data for a scene from a plurality of viewinglocations; acquiring spatially registered path-integrated gasconcentration measurements within a scene from a plurality of viewinglocations; and determining the location of a gas plume in 3D space bycombining the range data with the path-integrated gas concentration datausing a tomographic reconstruction algorithm.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying figures where like reference numerals refer toidentical or functionally similar elements and which together with thedetailed description below are incorporated in and form part of thespecification, serve to further illustrate an exemplary embodiment andto explain various principles and advantages in accordance with thepresent invention.

FIG. 1 is a diagram showing an example sensor for measuringspatially-registered target range and gas concentration measurements,according to a disclosed embodiment;

FIG. 2 is an image showing a sparse scan executed from three (3) viewinglocations, according to a disclosed embodiment;

FIG. 3 is an image showing a sparse scan executed from three (3)different viewing locations, according to a disclosed embodiment;

FIG. 4 is an image of the output of an occlusion processing simulation,according to a disclosed embodiment;

FIG. 5 is a top view of occlusion processing performed from multiplesensor perspectives, according to a disclosed embodiment;

FIG. 6 is an image of an output of a region growing segmentationalgorithm showing separation of large objects, according to a disclosedembodiment;

FIG. 7 is an image of the output of a plane/cylinder/other analysisshowing localization of the ground (black), large parts (gray) andcomplex parts (white), according to a disclosed embodiment;

FIG. 8 is a pair of images showing the filtering of segmented 3D spatialdata to identify specific components, or components with specificgeometric features, according to a disclosed embodiment;

FIG. 9 is a top-view image and a side-view image showing locations oflikely-to-leak components (highlighted in white) combined with wind datato define high-probability regions for detecting leaks, illustrated bythe transparent gray plume shapes, according to a disclosed embodiment;

FIG. 10 is a graph of spatially-registered 3D spatial data and gasconcentration data. Gas plumes are detected by finding regions in theC_(ave) image containing more than a predefined number of neighboringpoints exhibiting concentrations exceeding a predefined threshold. Thesource location for each gas plume, marked by (x) in the 3D topographyimage, is determined by finding the location of highest anomalousconcentration for each contiguous plume, according to a disclosedembodiment;

FIG. 11 is a histogram of C_(ave) data showing the expected/measuredbackground concentration and the background and elevated concentrationportions of the measurement distribution, according to a disclosedembodiment;

FIG. 12 is a measurement scene used to acquire 3D spatial data andpath-averaged CO₂ concentration images for the demonstration of leakdetection, localization and leaking component identification shown inFIG. 13 , according to a disclosed embodiment;

FIG. 13 is a set of images showing example of workflow for leakingcomponent identification, according to a disclosed embodiment;

FIG. 14 is a diagram showing a setup for demonstration of gas imagerflux measurements including a pipe emitting CO₂ at a rate regulated by amass flow controller, a fan to simulate wind, according to a disclosedembodiment;

FIG. 15 is a graph showing flux measurements of CO₂ performed usingGaussian plume fitting and simultaneous acquisition of target range andintegrated-path gas concentration measurements along the dashed-whitescan path of FIG. 14 , according to a disclosed embodiment;

FIG. 16 is an image of a path-integrated gas concentration imageindicating the locations of two planes used for tomographicreconstruction of concentration images, according to a disclosedembodiment;

FIG. 17 is a picture of the measurement scene containing the ‘CO₂shower’ with an overlaid path-integrated gas concentration image,according to a disclosed embodiment; and

FIG. 18 is a schematic of the measurement geometry of FIG. 17 ,according to a disclosed embodiment.

DETAILED DESCRIPTION

The current disclosure is provided to further explain in an enablingfashion the best modes of performing one or more embodiments of thepresent invention. The disclosure is further offered to enhance anunderstanding and appreciation for the inventive principles andadvantages thereof, rather than to limit in any manner the invention.The invention is defined solely by the appended claims including anyamendments made during the pendency of this application and allequivalents of those claims as issued.

It is further understood that the use of relational terms such as firstand second, and the like, if any, are used solely to distinguish onefrom another entity, item, or action without necessarily requiring orimplying any actual such relationship or order between such entities,items or actions. It is noted that some embodiments may include aplurality of processes or steps, which can be performed in any order,unless expressly and necessarily limited to a particular order; i.e.,processes or steps that are not so limited may be performed in anyorder.

1. Overview of Sensor Technologies and Associated Data

In the context of gas leak detection and monitoring, 3D spatial data canbe used independent of gas concentration measurements to understandmeasurement scene and define procedures that minimize measurement timewhile ensuring a desired level of confidence in the measurement results.Several factors may be considered when defining a good measurementprocedure. First, analysis of the 3D data can be used to inform theposition (or positions) from which measurements should be made to ensurecomprehensive viewing of the measurement scene. As an example, whenviewing a scene from only one sensor position, it is possible that aleak source may be occluded from view by structures or topography in thescene. And, depending on the application, it may be critical to ensurethat all regions that may contain gas plumes/leaks are sampled withsufficient measurement density to ensure reliable detection. The presentdisclosure teaches how one may determine the number and location ofsensor viewing positions to ensure sensor coverage of a scene to thedesired level. Second, the 3D data can be used to assign probabilitiesfor the likelihood of finding leaks as a function of location within themeasurement scene. As examples, certain spatial regions of a scene maybe more or less prone to leaks based on the infrastructure present inthe scene, or certain spatial regions of a scene may contain moreexpensive or dangerous assets that would represent a greater risk if aleak went undetected. These are two non-limiting examples of spatialregions of a scene that may be more important for monitoring than otherspatial regions of the scene. The present disclosure describes howknowledge of the 3D spatial data may allow for the creation ofnon-uniform gas measurement procedures that reduce the overallmeasurement time by spending more measurement resources, such asintegration time, point density, or averaging, measuring areas ofgreater importance and less measurement resources on areas of lessimportance. Furthermore, where it is available, wind data can beincluded to further improve the probability assignments and provideadditional localization of regions of greater importance, such as thosewith high probability for leak detection. Finally, 3D change detectioncan be implemented to identify changes in the topography of a scene. Iftopographic changes are detected, further analysis of the 3D spatialdata can be used to rapidly determine if changes to the gas measurementprocedure are required for adequate leak detection confidence. Detectionof topographic changes can also be used to alert operators to changes incritical infrastructure or their immediate surroundings.

When 3D spatial data is combined with gas concentration measurementsseveral advantages are realized for large-area gas monitoring and leakdetection tasks compared to the use of gas concentration measurementsalone. This is especially true for path-integrated gas concentrationmeasurements acquired using remote gas sensors based on opticalabsorption spectroscopy techniques such as wavelength modulationspectroscopy (See, e.g., Bomse, D. S., et. al., “Frequency modulationand wavelength modulation spectroscopies: comparison of experimentalmethods using a lead-salt diode laser,” Appl. Opt., 31, 718-731 (1992)),differential absorption LiDAR (See, e.g., Riris, H., et. al. “Airbornemeasurements of atmospheric methane column abundance using a pulsedintegrated-path differential absorption lidar.” Appl. Opt., 51, 34(2012).), and infrared absorption spectroscopy (See, e.g., the opticalgas imager camera offered by FLIR). Using optical absorptionspectroscopy the path-integrated concentration of a gas can be inferredby the attenuation of light traveling through the sample according tothe Beer-Lambert law,

$\begin{matrix}{P_{R} = {{P_{T}e^{{- 2}{\int_{0}^{l}{{\alpha(z)}dz}}}} = {{P_{T}e^{{- 2}\sigma C_{PI}}} = {P_{T}{e^{{- 2}\sigma C_{ave}l}.}}}}} & (1)\end{matrix}$

Here P_(T) is the light power transmitted through the gas sample, P_(R)is the power received by the gas sensor, α(z) is the gas absorption as afunction of distance along the measurement path. The path-integratedabsorption (∫₀ ^(l)α(z) dz) can be rewritten to express the laserabsorption in terms of the molecular absorption cross section σ, andeither the path-integrated gas concentration C_(PI), or path-averagedgas concentration C_(ave) and the path length of the gas sample 1. Forthis disclosure the gas sample path length l may also refer to thetarget range. Furthermore, when not specified the term gas concentrationcan refer to either the path-integrated or the path averaged gasconcentration.

FIG. 1 shows an example sensor 100 for measuring spatially-registeredtarget range and gas concentration measurements. In particular, FIG. 1is a diagram 100 of an example sensor configuration 110 for acquiringspatially-registered target range to a target 120 and integrated-pathgas concentration measurements of a gas plume 130 at measurement scene.

For the present disclosure, range may be considered synonymous withdistance. Also, target may be considered synonymous with surface andtopographical scatterer. For this example sensor, spatial registrationof both range and gas measurements is achieved by overlapping thetransmitted range and gas sensing beams while encoders measure theangular positions of both gimbal axes and record the direction of thetransmitted beams. This spatial registration enables the reconstructionof gas concentration imagery from collections of individual target rangeand gas concentration measurements. Gas concentration imageryreconstruction may be further supported by onboard GPS and inertialmeasurement unit (IMU) sensors that track the sensor position andorientation during measurements. GPS and IMU data may be essential forimage reconstruction in mobile sensing applications as the sensorposition and orientation can be changing during the measurements. Thisdata may also allow geo-registration of acquired data for both mobileand stationary measurement scenarios. Finally, the compact sensorpermits integration onto a variety of mobile platforms includingground-based vehicle, manned aircraft, and unmanned aircraft forlarge-area and potentially automated measurement procedures.

The first advantage of using 3D spatial data is that knowledge of thedistance to remote targets can be combined with path-integrated gasconcentration measurements to compute the path-averaged gasconcentration (C_(ave)) to points in a measurement scene. Measurementsof C_(ave) allow for straightforward detection of elevated (or otherwiseanomalous) regions of gas concentration in the measurement scene byremoving the ambiguity that arises in path-integrated gas concentrationmeasurements between changes in the target range and changes the averagegas concentration along the measurement path. The ability to moreprecisely and less ambiguously detect changes in remote gasconcentrations enables leak detection with higher-sensitivity andimproved confidence.

For example, the nominal atmospheric concentration of CO₂ is currentlyapproximately 400 ppm. To unambiguously attribute a change in thepath-integrated gas concentration C_(PI) of the 100 ppm-m to elevatedCO₂ levels along the measurement path, rather than an increased distanceto the topographic target, the distance to the target must be known tobetter than δR=100 ppm-m/400 ppm=25 cm. For measurements taken fromranges of tens to hundreds of meters it may be impossible to make such adistinction without a range measurement. Additional benefits ofcombining 3D spatial data with path-integrated gas concentrationmeasurements include improved leak detection confidence, leak sourcelocalization and identification through spatial imaging of gas plumes,quantification of the amount of gas measured in a scene compared to anexpected or nominal gas level, the use of shape detection to identifycomponents corresponding to leak sources, and gas flux estimation fordetected gas sources or sinks. Finally, wind data can be combined withC_(ave) imagery to improve leak localization, source identification, andflux estimation.

Sections 2 and 3 of this document provide examples and instructions forusing 3D spatial data with gas concentration measurements to supportincreased efficiency and automation of gas detection and monitoringtasks. The process may begin by accessing or acquiring a set of 3Dspatial data that has been collected from multiple perspectives so as toprovide full scene coverage. Such a 3D data set could be collected andassimilated in a one-time manner and stored so that subsequent scenevisits would benefit from the 3D data on file. Assimilation of 3Dspatial data taken from multiple perspectives into a single scenerepresentation can be achieved algorithmically with standardregistration algorithms (See, e.g., R. B. Rusu, N. Blodow, and M. Beetz,“Fast Point Feature Histograms (FPFH) for 3D registration,” IEEE Int.Conf. Robot., pp. 3212-3217, (2009)), or may be achieved automaticallywith accurate georeferenced 3D data. With a high-fidelity 3D map of ascene, a gas sensor can begin to exploit the scene features to optimizea variety of gas imaging tasks including: scan time minimization,topographic change detection, leak source localization, leakingcomponent identification, and leak rate quantification. The examplespresented here are aimed at detecting leaks in oil and gas productionfacilities, but the general concepts could be applied to a wide varietyof tasks that would benefit from large area and high spatial resolutiongas measurements.

2. Leak Detection Measurement Procedure Development Aided by 3D SpatialData

This section outlines methods for using 3D data to design gasmeasurement procedures that reduce measurement time while providingquantitative estimates of the confidence of a detection or non-detectionevent. To begin, consider a “brute force” gas measurement approach wherethe entirety of the 3D volume must be interrogated, regardless of scenetopography, to guarantee full scene analysis. In contrast to the bruteforce approach, we consider the possibility of “sparse” and “spatiallynon-uniform” scan approaches. In general, such approaches may assumethat the plume resulting from a leak is not isolated but instead hassome spatial extent. Thus, an appropriate sparse scan pattern maysupport leak detection with some likelihood despite under-sampling thevolume by design. 3D data can augment such scan approaches so as tobetter guarantee leak detection.

To highlight the possibilities, consider the effect of occlusion wherebyan object hides another object (or volume) from an observer. From asingle sensor location, for example, such occluded regions may hide leaksources and thereby prevent the leaks from being detected (falsenegative result). Or, one may assume that a large number of viewpointsmay be needed to reduce occluded regions to an acceptable level. With 3Ddata, it is possible to determine the number and location of sensorpositions to enable efficient coverage of a scene to the desired level.Through analysis of the 3D data, the gas imaging system can take stepsto mitigate the effect by altering viewing locations for maximum sparsescan coverage. An example of this concept is shown in FIGS. 2 and 3 .

FIG. 2 is an image 200 showing a sparse scan executed from three (3)viewing locations 210, 220, 230, according to a disclosed embodiment.Thin black lines represent the integration path of various concentrationmeasurements. Although the pattern should effectively cover the area ofinterest 240 (black box), the plume 250 (gray) is not interrogated dueto the occluding structures 260 (vertical black bars).

FIG. 3 is an image 300 showing a sparse scan executed from three (3)different viewing locations 310, 320, 330, according to a disclosedembodiment. Again, thin black lines represent the integration path ofvarious concentration measurements. By understanding the occlusionthrough analysis of spatial data, the viewing locations 310, 320, 330can be altered to guarantee coverage inside of the vertical bars 260.The plume 250 is correctly interrogated.

In order to optimize scan positions for maximum coverage, the 3D datamay be used to consider the effect of occlusion from an arbitraryviewing location. Line-of-sight algorithms that utilize the 3D spatialdata approximate the occlusion effect and can return only points presenton non-occluded surfaces from a given viewing location (See, e.g., thePoint Cloud Library). These non-occluded points may be termed “viewablesurfaces”. Implicit in this process is the ability to define whichregions of a given volume are also un-occluded or “viewable regions”.These regions are defined as the volumetric regions between the viewinglocation and the viewable surfaces. FIGS. 4 and 5 shows theimplementation of this algorithm on a solid model of a mock oil and gasproduction well pad with the viewable surfaces shaded gray.

FIG. 4 is an image 400 of the output of an occlusion processingsimulation. The gray points represent viewable surfaces of theunderlying model from the sensor perspective. FIG. 5 is a top view ofocclusion processing performed from multiple sensor perspectives 510,520, 530.

This algorithm can be executed from a variety of viewing locations toprovide quantitative estimates of the fraction of the scene that isviewable from each sensor perspective. The 3D spatial data and acollection of possible sensor perspectives can be combined in standardoptimization routines (See, e.g., the ‘fminsearch’ optimization functionin Matlab) to determine number and locations of sensor positionsrequired to view a specified fraction of the measurement scene.

The above discussion demonstrates a basic contribution of 3D data tomeasurement procedure optimization. However, 3D data presents furtheropportunities that can be leveraged to accelerate measurement time. Inmany leak detection cases, certain regions of a scene may be moreimportant than other regions. For example, certain components and/orlocations within an infrastructure are more likely to leak. By tailoringmeasurement procedures to acquire more point density, integration time,or averaging, monitoring areas in close proximity to these componentsand less measurement resources measuring where such components do notexist, scan time and leak detection probability can be furtheroptimized.

The identification of high probability leak areas may benefit from otheror additional 3D spatial data processing. First, segmentation is arobust method for separating 3D data of a structure into itsrepresentative parts, components, or elements each defining a contiguousstructure (See, e.g., the Point Cloud Library). These constituentelements can then be analyzed as needed in parallel by more complexalgorithms. A common segmentation algorithm is called region growing(See, e.g., the Point Cloud Library). Region growing may begin with thegeneration of a fast nearest-neighbor searchable data structure such asa kd-tree from the 3D spatial data. This data structure supportsmultiple tasks.

First, surface normal and curvature estimates may be generated. Next,low-curvature “seed” points may be randomly selected. For each seedpoint, the algorithm may iteratively “grow” a set of points describing agiven segment. At each iteration, the algorithm may search the datastructure for the nearest neighbors of each point in the set. Thenearest neighbors of each point may be appended to the set if theysatisfy geometric smoothness constraints based on quantities such astheir own curvature or the angular difference in surface normals. Theiteration may terminate when no new points are included in the givenset. The algorithm may then start again at a new seed. A common stoppingcondition is that some percentage of the full set of 3D points belongsto one of the segments.

An example output 600 is shown below in FIG. 6 . In particular, FIG. 6is an image 600 of an output of a region growing segmentation algorithmshowing separation of large objects (shaded to demonstrate theseparation). Borders and smaller complex objects are represented byblack points.

In cases where noise on the 3D spatial data degrades the output of thesegmentation algorithm, a smoothing and resampling filter such as amoving least squares surface reconstruction can be applied to the dataprior to segmentation.

Given the nature of common oil and gas production and distributioninfrastructure, two 3D shape “primitives” may be readily exploitable:the plane and the cylinder. Such planes and cylinders of larger sizesand smaller curvatures may be less likely to be sources of gas leaks,and may therefore be identified as less important regions in a scene toscan. By identifying larger and flatter objects that are wellrepresented by such primitives, smaller objects may be isolated, whichmay make them easier to identify and individually analyze. The regiongrowing algorithm above can be instructed to output large segments.These segments can then be analyzed with basic features such as thedistribution of surface normals and basic shape fits to identify them aseither planes, cylinders, or “other”, as shown in FIGS. 7 and 8 .

FIG. 7 is an image 700 of the output of a plane/cylinder/other analysisshowing localization of the ground (black), large parts (gray) andcomplex parts (white).

FIG. 8 is a pair of images 700,800 showing the filtering of segmented 3Dspatial data 700 to identify specific components, or components withspecific geometric features. In this case the filter selects onlycylindrical objects with radii in the intervals 19 cm-21 cm and 30 cm-31cm. More sophisticated filters can be constructed using spin images,covariance descriptors, point feature histograms, and graph approachesto identify specific components, with nearly arbitrary geometry, withina measurement scene.

The other category may include complex objects such as valves, smallpipe clusters, small utility boxes, etc. that are likely leak points.This information can be used to further tailor a measurement procedureto focus, in a non-spatially uniform manner, on these likely leaklocations. For a typical well pad scene we have observed that such“high-likelihood” leak points often constitute less than 10% of thesurface area of the scene.

The 3D data can afford the ability to further optimize the scan time. 3Dshape detection can allow for likely leak sites and large pieces ofequipment to be explicitly detected. For instance, with larger, flatterobjects identified and removed, the smaller objects can be processedthrough more advanced shape detection algorithms for specificidentification. Commonly used shape identification algorithms includebut are not limited to spin images, covariance descriptors, pointfeature histograms, and graph approaches (See, e.g., A. E. Johnson andM. Hebert, “Using spin images for efficient object recognition incluttered 3D scenes,” IEEE Transactions on Pattern Analysis and MachineIntelligence, vol. 21, no. 5, pp. 433-449, (1999); and D. Fehr, A.Cherian, R. Sivalingam, S. Nickolay, V. Morellas, and N.Papanikolopoulos, “Compact covariance descriptors in 3D point clouds forobject recognition,” in 2012 IEEE International Conference on Roboticsand Automation, pp. 1793-1798, (2012)). Often, the shape identificationworkflow may be decomposed into pose-invariant feature extraction, whichmay be followed by classification of the feature space. Training datacan be simulated or collected with the 3D topographic imaging system.Once a shape is identified, this information can then be used toincorporate a layer of context that may further define the probabilityof a leak occurring at that shape, likely constituents of a plume (i.e.methane, water vapor, VOCs, etc.), or possible leak rates. Contextualrelationship maps may incorporate the relative position of objects tobetter identify the objects and to rate their significance.State-of-the-art algorithms refer to this as semantic labeling.

Once identified and rated, high leak probability regions within the 3Dspatial data can be combined with wind velocity data to definemeasurement volumes where detection of gas plumes is likely if a leak ispresent. An example of this processing step is shown in FIG. 9 , and isbased on a down-selected set of the well pad components and featuresidentified in FIG. 7 .

FIG. 9 is a top-view image 900 and a side-view image 910 showinglocations of likely-to-leak components (highlighted in white) and winddata used to define high-probability regions for detecting leaks,illustrated by the transparent gray plume shapes.

The defined measurement volumes, shaded in gray, occupy less than 5% ofthe volume and less than 25% of the area—as viewed from above—of thetotal well pad scene. By heavily weighting the measurement procedure onthese regions the measurement time for this scene can be reduced by afactor of 2 to 3. 3D spatial data combined with wind data can facilitateadditional specificity and accuracy for defining measurement volumesthrough the use of computational fluid dynamics (CFD) (See, e.g., onlinetutorials for the open source computational fluid dynamics softwareOpenFOAM). Detailed wind velocity fields can be computed for themeasurement scene with initial conditions supplied by wind velocitymeasurements using a variety of CFD programs such as Open FOAM andANSYS. Wind velocity fields may allow algorithms that define themeasurement volumes for a scene to account for more complex gastransport behaviors near objects such as changes in wind speed anddirection, backflow regions, and eddy currents.

3. Leak Detection, Localization, Quantification and SourceIdentification

This section presents methods for combining 3D spatial data with gasconcentration measurements to detect, localize and quantify gas leaksand to identify the component corresponding to the leak source.

A first step in this process may be leak detection. A significantproblem with existing leak detection methods and technologies is theoccurrence of false detection events. Here, 3D spatial data affordssubstantial benefits over existing state-of-the-art leak monitoringtechniques. The ability to compute the path-averaged average gasconcentration along a measurement direction can enable extremelysensitive detection of elevated (or otherwise anomalous) gasconcentrations, even for gas species with non-zero nominal atmosphericconcentrations. Furthermore, the capability to spatially registerindividual measurements to generate C_(ave) images may allow additionaldiscrimination based on the proximity, continuity and spatial extent ofanomalous detections to greatly reduce the probability of falsedetections. For example, the C_(ave) image in FIG. 10 was created bycombining laser ranging distance measurements (3D topography image) withsimultaneously acquired path-integrated CO₂ concentration measurements.The C_(ave) image shows two CO₂ plumes emanating from the ground thatleaked from a pipe buried 6′ below the surface at a rate of 54 kg/day. Ahistogram of the C_(ave) image, FIGS. 10 and 11 illustrate thehigh-sensitivity detection of anomalous CO₂ concentrations enabled bythis technique.

FIG. 10 is a graph 1000 of spatially-registered 3D spatial data and gasconcentration data. Gas plumes are detected by finding regions in theC_(ave) image containing more than a predefined number of neighboringpoints exhibiting concentrations exceeding a predefined threshold. Thedetermined location of two identified leaks are marked (x) on the 3Dspatial image.

FIG. 11 is a histogram 1100 of the C_(ave) data showing theexpected/measured background concentration and the background andelevated concentration portion of the measurement distribution.

The most frequent occurrences in the histogram 1100 corresponds to thenominal atmospheric CO₂ background level that covers most of the image.The distribution of background CO₂ has a roughly Gaussian shape with 1/ehalf-width of 5 ppm. The narrow width of the background distributionallows clear distinction between background and elevated measurementsthat forms the basis of the leak detection and characterization stepspresented herein.

An effective algorithm for robust leak detection based on C_(ave) imagescould have the following steps:

(1) Find points in the image that exceed a predefined concentrationthreshold for leak detection. Some of these points may be spuriousmeasurements which could cause false positives.

(2) Generate a nearest-neighbor searchable data structure such as akd-tree from the 3D spatial data.

(3) Search the 3D spatial data to find a predefined number of nearestneighbors surrounding each point identified in step (1).

(4) Query the nearest neighbors found in step (3) to compute the numberof neighboring points that also exhibit elevated gas concentration.

(5) Since it is unlikely that spurious measurements would be locatednear one another spatially, one may report a leak if the number ofspatially neighboring points exhibiting elevated concentration exceeds apredefined threshold.

This leak detection algorithm can easily be expanded to discriminatebased on additional plume properties, such as spatial extent. Consider aset of points in the C_(ave) image that resulted in a positive leakdetection based on steps 1 through 5. A spatial extent threshold forplume detection can be applied by seeding a region growing algorithm atthe location of the detected leak, based on concentration, to divide thescene into two segments representing the plume and the rest of thescene. The 3D spatial data can then be used to estimate the areaoccupied by the detected plume, which can then be compared against apredefined threshold for leak detection. By designing an appropriate setof parameters and thresholds for leak detection, the probability forfalse detection of a leak can be greatly reduced.

Once a leak has been detected, the 3D spatial data can be leveraged todetermine the total quantity of leaked gas in the measurement scene aswell as the location of the leak source. As a possible first step, theexpected background concentration is subtracted from C_(ave) resultingin an image of the anomalous path-averaged gas concentration within themeasurement scene. The expected background level can be estimated fromthe C_(ave) image (e.g. the centroid of the Gaussian portion of thehistogram distribution for the background), or based on supplementaryinformation. As a possible next step, each point within thebackground-subtracted C_(ave) image may be multiplied by itscorresponding target range to form an image of the path-integratedconcentration of the anomalous gas (C_(anom)) within the measurementscene. As a possible final step, the location of maximum anomalous gasconcentration within the C_(anom) image may be designated as the leaksource. This location can be determined by a number of methods includingGaussian plume fitting, a gradient search of smoothed C_(anon), data orby implementing a derivative-free optimization algorithm on the C_(anom)image. Further interrogation of the 3D data with occlusion processingcan be used to estimate the probability that the leak source resides ona viewable surface. If this step uncovers a significant likelihood thatthe leak resides on an unviewable surface the 3D data can be used toestimate possible locations of the true leak source. The outcome of thisanalysis can inform a decision to acquire additional C_(ave)measurements from a different viewing perspective, and provide optionsfor the optimal viewing locations.

After the leak has been located, the 3D data can be leveraged yet againto determine the topographic feature or component at the location of theleak source. As described in the previous section, most objectidentification procedures rely on layers of contextual informationassociated with the 3D data. The quantity and detail of the contextualinformation may dictate the feature identification approach that is bestsuited for a given measurement case and may determine the specificity ofobject identification that can be achieved. In cases where limited or nocontextual information is available, the 3D data near the leak sourcecan be analyzed via segmentation. An example of this approach is shownin FIG. 13 , and is based on co-acquired 3D topography and gasconcentration measurements of the scene shown in FIG. 12 . First, thelocation of the gas plume may be determined from the gas concentrationimage. Next, the surface normals and curvature of the 3D spatial datanear the gas plume may be computed and inputted into a region growingalgorithm to find regions of high curvature within the measurementscene. The output of this step produces an image segment at the locationof the gas plume corresponding to the leak source. The next step can usea piece of contextual information from the measurement scene picture inFIG. 12 .

FIG. 12 is a measurement scene 1200 used to acquire 3D spatial data andpath-averaged CO₂ concentration images for the demonstration of leakdetection, localization and leaking component identification shown inFIG. 13 .

FIG. 13 is a set 1300 of images 1310, 1320, 1330, 1340 showing exampleof workflow for leaking component identification. (1) 3D spatial dataacquired via spatially-scanned laser ranging is filtered with a movingleast squares filter followed by computation of surface normals andlocal curvature. (2) A region growing algorithm is used to segmentregions of high curvature within the scene. (3) The leak source locationis determined using in the gas concentration image. (4) Shape fitting isapplied to segmented regions to identify components near the leaklocation.

The picture shows the object at the leak location that appears to be apipe with diameter of roughly 4″. Using this information a cylindricalshape fit is applied to all image segments identified in FIG. 13 1320and the segments are ranked based on the residual fit error. The imagein FIG. 13 1340 shows the output of a shape fitting filter wherein thepipe, located at the leak source, exhibited the lowest residual shapefit errors.

Object identification can be extremely effective in cases where morecontextual information is available. For instance, if the 3D spatialdata is geo-registered, the geo-location of the leaking component may beidentified through localization of the leak source. In this case,contextual information consisting of a list of components in the sceneand their GPS locations may be sufficient to positively identify theleaking component. More sophisticated and generalized objectidentification can be achieved through shape detection. Here, the 3Ddata may be used to create a library of components within themeasurement scene, and pose-invariant shape detection algorithms may beimplemented on sets of measured 3D data to uniquely identify individualcomponents (See, e.g., Karmacharya, A., Boochs, F. & Tietz, B.“Knowledge guided object detection and identification in 3D pointclouds.” SPIE 9528,952804-952804-13 (2015)).

The final leak quantification method disclosed herein enablesdeterminations of the rate or flux of a detected leak. To illustrate theapproach, an example gas flux measurement performed in a controlledenvironment is shown in FIGS. 14 and 15 .

FIG. 14 is a diagram 1400 showing a setup for demonstration of gasimager flux measurements including a pipe emitting CO₂ at a rateregulated by a mass flow controller and a fan to simulate wind. Scanpatterns used for flux measurements are indicated by dashed-white andsolid white lines.

FIG. 15 is a graph 1500 showing flux measurements of CO₂ performed usingGaussian plume fitting and simultaneous acquisition of target range andintegrated-path gas concentration measurements along the dashed-whitescan path of FIG. 14 .

The picture in FIG. 14 shows the measurement scene consisting of avertical pipe that emits CO₂ at a rate determined by a mass flowcontroller. A fan is positioned near the leak source to simulate wind,and a 2-dimensional anemometer was used to measure the wind velocity,roughly 1 m/s, at the leak source. Prior to flux rate estimation,high-resolution 3D topography and gas concentration images of the leakarea were acquired to determine the location and extent of the gasplume, and to inform the choice of leak rate scan pattern.

The high-resolution plume image and two possible scan patterns for leakrate estimation are overlaid on the measurement scene picture in FIG. 14. The two scan patterns are designed to optimize different aspects ofthe flux measurement. Both patterns transect the plume in a directionapproximately perpendicular to the flow. This can be important sinceperpendicular transects may produce the lowest noise flux measurementsdue to fluctuations in the wind velocity and plume concentration. Bothpatterns also form a closed volume between the sensor and the targetsurface, such that no gas can enter or escape the enclosed volumewithout passing through the measurement beam. The two patterns differ inthat one encloses the leak source, while the other transects the plumetwice at different distances from the leak source. Enclosing the leaksource may be desirable because it can enable discrimination between gassources originating within the enclosed scan pattern from those locatedoutside the scan pattern. A leak-enclosing pattern may be favored insituations where multiple gas sources are present in the measurementscene. On the other hand, the scan that transects the plume twice mayenable estimation of the gas velocity, even without an independent windmeasurement, via temporally correlating plume parameters at the twotransect locations. This method for estimating gas velocity is akin toblock matching techniques used to estimate flux from camera-based gasabsorption images (See, e.g., Sandsten, J., et. al., “Volume flowcalculations of gas leaks imaged with infrared gas-correlation.” Opt.Exp., 20, 20318-20329 (2012)). Plume parameters that can be temporallycorrelated to estimate wind data at spatially separated transectlocations include the plume centroid location, plume shape and plumeconcentration.

To estimate the gas flux (Q), the plume transect measurements can be fitwith Gaussian plume model,

$\begin{matrix}{{C = {\frac{Q}{2\pi u\sigma_{y}\sigma_{z}}{e^{\frac{- y^{2}}{2\sigma_{y}^{2}}}\left\lbrack {e^{\frac{- {({z - H})}^{2}}{2\sigma_{z}^{2}}} + e^{\frac{- {({z + H})}^{2}}{2\sigma_{z}^{2}}}} \right\rbrack}}},} & (2)\end{matrix}$

where C is the gas concentration as a function of spatial coordinates yand z, u is the gas velocity, σ_(y) and σ_(z) are the standarddeviations of the plume distribution in the y and z directions and H isthe plume centroid in the z-direction.

The measurements in FIG. 15 were acquired with the dashed scan patternat a rate of 4 scans per second, and analyzed with Gaussian plumefitting. Eight individual transect measurements were averaged yieldingupdated flux estimates at 2 second intervals. Over the course of 120seconds the mass flow rate of CO₂ was stepped in intervals of 10 litersper minute from 0 lpm to 40 lpm and back to 0 plm. The measured CO₂ fluxestimates show good agreement with the mass controller settings for thistest consistently registering within 10% of the set value at each step.Another way to estimate the gas flux Q is to multiply the gas speed bythe integrated anomalous gas concentration along the plume transect. Inthis case the flux estimate is given by,

Q=uΣ _(i) ^(N) C _(anom) Δy,  (3)

where N is the number of C_(anom) measurements along the plume transectand Δy is the spacing between C_(anom) measurements at the location ofthe plume. This method has the benefit that it does not require fittingand it works for plumes of any shape.

A requirement for accurate estimates of the gas flux (Q) may beknowledge of the distance from the sensor to the gas plume for properscaling of the spacing between C_(anom) measurements Δy or the plumestandard deviations, σ_(y) and σ_(z), depending on the estimationtechnique being used. Such information may be difficult to ascertainfrom a single measurement perspective because a plume with small σ_(y)and σ_(z) located close to the sensor can appear similar in gasconcentration imagery as a plume with large σ_(y) and σ_(z) locatedfarther from the sensor. The situation is simplified for the measurementscenario in FIG. 14 as the flux measurement is performed close to thepipe emitter, and the range from the sensor to the pipe is measured inthe 3D topography data. In cases where the plume is located further fromsurfaces in the measurement scene it may be necessary to localize theplume within the measurement volume to get an adequate estimate of thedistance from the sensor to the plume transect being analyzed.Volumetric localization can be accomplished by measuring the plume frommore than one perspective, and performing gas absorption tomography(See, e.g., Twynstra, M. G. and Duan, K. J., “Laser-absorptiontomography beam arrangement optimization using resolution matrices,”Applied Optics, 29, 7059-7068 (2012)). An example of tomography forplume localization is shown in FIGS. 16-18 .

FIG. 16 is an image 1600 of a path-integrated gas concentration imageindicating the locations of two planes 1610, 1620 used for tomographicreconstruction of concentration images. The reconstructed concentrationimagers have 0.3 m voxel resolution in the x and y dimensions.Resolution in the z dimension depends on the density of reconstructedplanes.

FIG. 17 is a picture 1700 of the measurement scene containing the ‘CO₂shower’ with an overlaid path-integrated gas concentration image.

FIG. 18 is a schematic 1800 of the measurement geometry of FIG. 17 .Tomographic CO₂ concentration reconstructions are enabled by combiningpath-integrated CO₂ concentration measurements and target rangemeasurements from multiple sensor positions.

FIG. 17 shows the measurement scene with an overlaid CO₂ concentrationimage of a plume falling from an elevated pipe. FIG. 18 provides aschematic of the sensor positions from which subsequent coarseresolution scans of the plume are performed. Coarse spatial resolutionmeasurements may be used for plume tomography so measurements frommultiple perspectives can be acquired before the plume location changesappreciably. FIG. 16 shows tomographic reconstructions of the plume attwo transects that result in determinations of the y-direction distanceto the plume from the sensor at each transect. In general, thetomographic reconstruction of gas concentration may be performed bysuperposing a grid of N cells on the reconstruction plane and invertingthe equation,

b _(i)=Σ_(j) ^(N) A _(ij) x _(j),  (4)

where b_(i) is the molar fraction integrated-path gas concentrationmeasurement along the i^(th) measurement direction, A_(ij) is the chordlength along the i^(th) direction inside the j^(th) grid cell and x_(j)is the molar fraction gas concentration in the j^(th) grid cell. Withspatially coarse measurements it can be difficult to acquire sufficientconcentration measurements (b_(i)) to invert equation 4 directly.Conversely, taking the time to acquire higher spatial resolution gasmeasurements at many sensor positions can allow the plume position toevolve during the measurement, which also hinders tomographicreconstruction. This problem can be overcome by rapidly acquiring coarsespatial resolution measurements and applying one of a number oftechniques for spanning the null space of the under-sampledreconstruction grid. Examples include Tikihonov regularization,interpolation of the concentration measurements (b_(i)) or Gaussianfitting of the plumes measured from each position (See, e.g., Twynstra,M. G. and Duan, K. J., “Laser-absorption tomography beam arrangementoptimization using resolution matrices,” Applied Optics, 29, 7059-7068(2012)).

In summary, the methods for leak detection and characterizationdisclosed herein enable the determination of the leak location, leakquantification, and identification of equipment that is the likely leaksource. As the source of the leak may be a surface in the scene, thesearch procedure can be greatly accelerated with the use of 3D spatialdata. Equipment or features identified in the 3D spatial data can beranked according to likelihood as a leak source to define efficientmeasurement procedures. When a leak is detected and localized, the 3Dinformation can be compared to the location of the detected plume andthe environmental conditions (i.e. wind direction) to quickly identifythe most likely leak sources. Elevated gas concentration near thepossible leak source can confirm or deny each hypothesis. Once aspecific leak site is identified, the system can follow up with gasquantification measurements and a high-resolution measurement of theequipment demonstrating the leak. This process can give site managersactionable information. For example, a dispatch engineer may know whichpart needs to be repaired or replaced before ever visiting the site.

1.-20. (canceled)
 21. A method comprising: identifying an object in ascene based on 3D spatial data of the scene; identifying a region ofanomalous gas concentration in the scene based on a gas concentrationmeasurement; assigning one or more spatial coordinates of the object;assigning one or more spatial coordinates of the region of anomalous gasconcentration; and determining a spatial relationship between the objectand the region of anomalous gas concentration using the one or morespatial coordinates of the object and the one or more spatialcoordinates of the anomalous gas concentration.
 22. The method of claim21, further comprising identifying the object as a source of the regionof anomalous gas concentration based on the spatial relationship betweenthe object and the region of anomalous gas concentration.
 23. The methodof claim 21, further comprising collecting the 3D spatial data of thescene with a laser.
 24. The method of claim 23, further comprisingcollecting the 3D spatial data from multiple perspectives of the scene.25. The method of claim 21, wherein identifying the object includessegmenting the 3D spatial data.
 26. The method of claim 25, furthercomprising: applying 3D shape primitives to the segmented 3D spatialdata to determine properties of the object including a size of theobject, a shape of the object, or combinations thereof; and identifyingthe object based on the determined properties of the object.
 27. Themethod of claim 21, further comprising collecting the gas concentrationmeasurement based on laser spectroscopy.
 28. The method of claim 27,wherein the gas concentration measurement includes a path-integrated gasconcentration measurement.
 29. The method of claim 21, whereindetermining the spatial relationship includes determining a distancebased on the one or more spatial coordinates of the object and the oneor more spatial coordinates of the anomalous gas concentration.
 30. Themethod of claim 21, further comprising: determining a wind velocityfield based on a wind measurement and the 3D spatial data; anddetermining the spatial relationship between the object and the regionof anomalous gas concentration based on the wind velocity field.
 31. Amethod comprising: identifying an object in a scene based on 3D spatialdata of the scene; identifying a region of anomalous gas concentrationin the scene based on a gas concentration measurement; determining ifthe object is a source of the region of anomalous gas concentrationbased on a spatial relationship between the object and the region ofanomalous gas concentration.
 32. The method of claim 31, furthercomprising: assigning one or more spatial coordinates to the object;assigning one or more spatial coordinates to the region of anomalous gasconcentration; and determining if the object is the source of theanomalous gas concentration based on a relationship between the one ormore spatial coordinates of the object and the one or more spatialcoordinates to the region of anomalous gas concentration.
 33. The methodof claim 32, further comprising determining a distance between objectand the region of anomalous gas concentration based on the one or morespatial coordinates of the object and the one or more spatialcoordinates to the region of anomalous gas concentration.
 34. The methodof claim 31, further comprising: segmenting the 3D spatial data of thescene; and identifying the object based on the segmented 3D spatialdata.
 35. The method of claim 34, further comprising: applying 3D shapeprimitives to the segmented 3D spatial data to determine a size of theobject, a shape of the object, or combinations thereof.
 36. The methodof claim 31, further comprising: determining a wind velocity field basedon a wind measurement and the 3D spatial data; and determining if theobject is the source of the region of anomalous gas concentration basedon the wind velocity field.
 37. The method of claim 31, furthercomprising collecting the 3D spatial data with a laser.
 38. The methodof claim 37, further comprising collecting the 3D spatial data frommultiple perspectives of the scene.
 39. The method of claim 31, furthercomprising collecting the gas concentration measurement using laserspectroscopy.
 40. The method of claim 31, wherein the gas concentrationmeasurement includes a path-integrated gas concentration measurement.